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How Early Is Too Early? Identification of Elevated, Persistent Problem Behavior in Childhood

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Abstract

We inquire how early in childhood children most at risk for problematic patterns of internalizing and externalizing behaviors can be accurately classified. Yearly measures of anxiety/depressive symptoms and aggressive behaviors (ages 6–13; n = 334), respectively, are used to identify behavioral trajectories. We then assess the degree to which limited spans of yearly information allow for the correct classification into the elevated, persistent pattern of the problem behavior, identified theoretically and empirically as high-risk and most in need of intervention. The true positive rate (sensitivity) is below 70% for anxiety/depressive symptoms and aggressive behaviors using behavioral information through ages 6 and 7. Conversely, by age 9, over 90% of the high-risk individuals are correctly classified (i.e., sensitivity) for anxiety/depressive symptoms, but this threshold is not met until age 12 for aggressive behaviors. Notably, the false positive rate of classification for both high-risk problem behaviors is consistently low using each limited age span of data (< 5%). These results suggest that correct classification into highest risk groups of childhood problem behavior is limited using behavioral information observed at early ages. Prevention programming targeting those who will display persistent, elevated levels of problem behavior should be cognizant of the degree of misclassification and how this varies with the accumulation of behavioral information. Continuous assessment of problem behaviors is needed throughout childhood in order to continually identify high-risk individuals most in need of intervention as behavior patterns are sufficiently realized.

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Data Availability

The data used in this research are available by request from the principle investigators of the Rochester Intergenerational Study and the University at Albany. Current efforts are underway to publish the data through ICPSR in line with the funding requirements.

Notes

  1. Since ages in the first wave of data collection for the RIGS study ranged from 2 to 13 years old (Thornberry et al. 2018), all RIGS participants who were 8 years or older at the start of RIGS were excluded from the analysis (N = 92). Additionally, some RIGS children were too young to be included in the analysis as they were not at least 12 years of age in 2017 (n = 32), the last year of completed data collection. The remaining children had missing information in more than one interview during the defined observation period (n = 81).

  2. As an alternative to GBTM, we could have instead modeled the trajectories using growth mixture modeling (GMM; Muthén 2001). As GMM principally distinguishes latent classes by the shape of the curve, with within-class variation captured by a variance component for the growth parameter(s), this approach tends to yield fewer latent classes than GBTM. In the interest of parsimony for classification (i.e., membership in a latent class as a binary risk factor), we thus choose to use GBTM which would likely yield distinct latent classes, specifically a high-level group, rather than a continuous latent construct which would be harder to classify in our schema.

  3. To be clear, the Bayes rule calculation used to calculate the posterior probabilities at earlier ages is a function of (a) the growth parameters from the model estimated using all time points, and (b) the vector of data observed only through age t, rather than age T. The former implies greater precision of the actual parameter estimates (see Petras 2016).

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Funding

Support for RYDS and RIGS has been provided by the National Institute on Drug Abuse (R01DA020195, R01DA005512), the Office of Juvenile Justice and Delinquency Prevention (86-JN-CX-0007, 96-MU-FX-0014, 2004-MU-FX-0062), the National Science Foundation (SBR-9123299), and the National Institute of Mental Health (R01MH56486, R01MH63386). Technical assistance for RYDS/RIGS was provided by an NICHD grant (R24HD044943) to The Center for Social and Demographic Analysis at the University at Albany.

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Authors and Affiliations

  1. The University of Texas at San Antonio, 501 W. Cesar Chavez Blvd., San Antonio, TX, 78207, USA

    Megan Bears Augustyn

  2. Pennsylvania State University, Pennsylvania, PA, USA

    Thomas Loughran

  3. Pontifica Universidad Catolica de Chile, Santiago, Chile

    Pilar Larroulet Philippi

  4. The University of Maryland, College Park, MD, USA

    Terence P. Thornberry

  5. Colorado State University, Fort Collins, CO, USA

    Kimberly L. Henry

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  1. Megan Bears Augustyn
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Correspondence to Megan Bears Augustyn.

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Appendices

Appendix A. Individual Items in CBCL Subscales

CBCL Anxiety/Depressive Symptom Subscale Items

During the past six months, how is it true that [child]…

1. Complains of loneliness?

2. Cries a lot?

3. Fears (he/she) might think or do something bad?

4. Feels (he/she) has to be perfect?

5. Feels or complains that no one loves (him/her)?

6. Feels others are out to get (him/her)?

7. Feels worthless or inferior?

8. Is nervous, high-strung, or tense?

9. Is too fearful or anxious?

10. Feels too guilty?

11. Is self-conscious or easily embarrassed?

12. Is suspicious?

13. Is unhappy, sad, or depressed?

14. Worries?

CBCL Aggression Subscale Items

During the past six months, how is it true that [child]…

1. Argues a lot?

2. Brags or boasts?

3. 17Is cruel, bullying, or mean to others?

4. Demands a lot of attention?

5. Destroys (his/her) own things?

6. Destroys things belonging to (his/her) family or others?

7. Is disobedient at home?

8. Is disobedient at school?

9. Is easily jealous?

10. Gets into many fights?

11. Physically attacks people?

12. Screams a lot?

13. Clowns or shows off?

14. Is stubborn, sullen, or irritable?

15. Has sudden changes in (his/her) mood or feelings?

16. Talks too much?

17. Teases a lot?

18. Has temper tantrums or a hot temper?

19. Threatens people?

20. Is unusually loud?

Appendix B. Trajectory Model Diagnostics

Panel A. Anxiety/Depressive Symptoms

 

95% CI

 

Group

\( \hat{\pi} \)

lower

upper

\( \hat{p} \)

Ave PP

Odds CC

Low

0.545

0.486

0.604

0.551

0.967

24.7

Moderate

0.331

0.275

0.387

0.326

0.952

40.2

High

0.124

0.088

0.160

0.123

0.986

503.4

Panel B. Aggression Trajectories

 

95% CI

 

Group

\( \hat{\pi} \)

lower

upper

\( \hat{p} \)

Ave PP

Odds CC

Low

0.428

0.359

0.497

0.434

0.955

28.1

Moderate

0.408

0.347

0.469

0.404

0.934

20.4

High

0.164

0.106

0.222

0.162

0.942

82.9

  1. Note: Both sets of trajectories pass all four key model adequacy diagnostics recommended by Nagin (2005)

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Augustyn, M.B., Loughran, T., Philippi, P.L. et al. How Early Is Too Early? Identification of Elevated, Persistent Problem Behavior in Childhood. Prev Sci 21, 445–455 (2020). https://doi.org/10.1007/s11121-019-01060-y

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